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Multi-Depot Open Vehicle Routing Problem with Time Windows Based on Carbon Trading
Ling Shen1, Fengming Tao2,3, Songyi Wang4
1College of Mechanical Engineering, Chongqing University, Chongqing 400044, China. shenlingling@cqu.edu.cn.
International Journal of Environmental Research and Public Health
|September 20, 2018
Summary
This study introduces a low-carbon multi-depot open vehicle routing problem with time windows (MDOVRPTW) model to reduce logistics costs and emissions. A novel two-phase algorithm using particle swarm optimization and tabu search optimizes routes under carbon trading policies.
Area of Science:
- Operations Research
- Environmental Economics
- Logistics Management
Background:
- Rising logistics costs and the imperative for sustainable practices necessitate innovative routing solutions.
- Chinese government's low-carbon economy initiatives drive the need for integrating carbon trading into logistics planning.
- The open vehicle routing problem presents complex challenges in real-world logistics operations.
Purpose of the Study:
- To develop a model for the low-carbon multi-depot open vehicle routing problem with time windows (MDOVRPTW).
- To minimize total logistics costs, including driver salaries, penalties, fuel, and carbon trading expenses.
- To propose an effective algorithm for solving the MDOVRPTW under carbon emission constraints.
Main Methods:
- Construction of a mathematical model for the MDOVRPTW incorporating carbon trading policies.
- Development of a two-phase algorithm: Particle Swarm Optimization (PSO) for initial solutions and Tabu Search (TS) for global optimization.
- Experimental validation of the algorithm's performance, particularly for small-scale cases.
Main Results:
- The proposed two-phase algorithm effectively solves the MDOVRPTW, demonstrating suitability for small-scale logistics problems.
- Analysis reveals that variations in carbon trading prices and quotas significantly impact total costs, carbon trading expenses, and overall emissions.
- The study quantifies the relationship between carbon policy parameters and logistics operational outcomes.
Conclusions:
- The developed MDOVRPTW model and algorithm provide a practical framework for optimizing logistics routes under carbon constraints.
- Findings offer actionable insights for policymakers to refine carbon trading strategies.
- Logistics companies can leverage these results for improved route planning and cost management in a low-carbon economy.
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